/// <summary> /// Convert a CSV file to binary. /// </summary> /// <param name="csvFile">The CSV file to convert.</param> /// <param name="format">The format.</param> /// <param name="binFile">The binary file.</param> /// <param name="input">The input.</param> /// <param name="ideal">The ideal.</param> /// <param name="headers">True, if headers are present.</param> public static void ConvertCSV2Binary(FileInfo csvFile, CSVFormat format, FileInfo binFile, int[] input, int[] ideal, bool headers) { binFile.Delete(); var csv = new ReadCSV(csvFile.ToString(), headers, format); var buffer = new BufferedMLDataSet(binFile.ToString()); buffer.BeginLoad(input.Length, ideal.Length); while (csv.Next()) { var inputData = new BasicMLData(input.Length); var idealData = new BasicMLData(ideal.Length); // handle input data for (int i = 0; i < input.Length; i++) { inputData[i] = csv.GetDouble(input[i]); } // handle input data for (int i = 0; i < ideal.Length; i++) { idealData[i] = csv.GetDouble(ideal[i]); } // add to dataset buffer.Add(inputData, idealData); } buffer.EndLoad(); buffer.Close(); csv.Close(); }
/// <summary> /// Convert a CSV file to a binary training file. /// </summary> /// <param name="csvFile">The CSV file.</param> /// <param name="format">The format.</param> /// <param name="binFile">The binary file.</param> /// <param name="inputCount">The number of input values.</param> /// <param name="outputCount">The number of output values.</param> /// <param name="headers">True, if there are headers on the3 CSV.</param> /// <param name="expectSignificance">Should a significance column be expected.</param> public static void ConvertCSV2Binary(String csvFile, CSVFormat format, String binFile, int inputCount, int outputCount, bool headers, bool expectSignificance) { new FileInfo(binFile).Delete(); var csv = new CSVMLDataSet(csvFile, inputCount, outputCount, false, format, expectSignificance); var buffer = new BufferedMLDataSet(binFile); buffer.BeginLoad(inputCount, outputCount); foreach (IMLDataPair pair in csv) { buffer.Add(pair); } buffer.EndLoad(); }
/// <summary> /// Called to generate the training file. /// </summary> public void Generate() { string[] list = Directory.GetFiles(_path); _trainingFile.Delete(); var output = new BufferedMLDataSet(_trainingFile.ToString()); output.BeginLoad(Config.InputWindow, 1); foreach (string file in list) { var fn = new FileInfo(file); if (fn.Name.StartsWith("collected") && fn.Name.EndsWith(".csv")) { ProcessFile(file, output); } } output.EndLoad(); output.Close(); }